Electrical Biosensing at Physiological Ionic Strength Using Graphene Field-Effect Transistor in Femtoliter Microdroplet.

Clicks: 370
ID: 44466
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #15 of 275 articles by views in Nano letters

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 275 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Graphene has strong potential for electrical biosensing owing to its two-dimensional nature and high carrier mobility which transduce the direct contact of a detection target with a graphene channel to a large conductivity change in a graphene field-effect transistor (G-FET). However, the measurable range from the graphene surface is highly restricted by Debye screening, whose characteristic length is less than 1 nm at physiological ionic strength. Here, we demonstrated electrical biosensing utilizing the enzymatic products of the target. We achieved quantitative measurements of a target based on the site-binding model and real-time measurement of the enzyme kinetics in femtoliter microdroplets. The combination of a G-FET and microfluidics, named a "lab-on-a-graphene-FET", detected the enzyme urease with high sensitivity in the zeptomole range in 100 mM sodium phosphate buffer. Also, the lab-on-a-graphene-FET detected the gastric cancer pathogen Helicobacter pylori captured at a distance greater than the Debye screening length from the G-FET.
Reference Key
ono2019electricalnano Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ono, Takao;Kanai, Yasushi;Inoue, Koichi;Watanabe, Yohei;Nakakita, Shin-Ichi;Kawahara, Toshio;Suzuki, Yasuo;Matsumoto, Kazuhiko;
Journal Nano letters
Year 2019
DOI
10.1021/acs.nanolett.9b01335
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.